Abstract: Metastasis from the primary tumor site to the brain is the most lethal complication of cancer progression and is experienced by approximately 20% of breast cancer patients worldwide. There is at present no translational approach to detect if a primary tumor has brain metastatic potential, no markers that predict successful future metastasis, and thus no therapies to target any of the processes involved. These gaps are difficult to bridge due to a lack of technology that can classify a cancer cell?s brain metastatic potential. Current in vivo murine models are slow to manifest metastasis and do not have the capability of capturing single cell morphology and dynamics; therefore, we propose a diagnostic platform to measure the phenotypic differences between cancer cells and to assign them a brain metastatic potential. The output is a quantitative diagnostic read out that defines the probability of a patient's cell metastasizing to the brain. Preliminary data suggests we may use this platform to brain metastatic behavior in 24-72 hrs. We have demonstrated that we can classify non-brain seeking and brain seeking cell lines based on phenotypic metrics such as migration, extravasation, shape and volume with a positive predictive value of 0.9. This study will validate the performance of this platform on patient cells. Further we aim to understand what components of the brain stromal space promote brain metastasis to further improve the performance of this technology and identify candidates that could be targeted by therapeutics to prevent metastasis in patients that have been identified as at risk. In summary, we propose a unique approach to measure the individual metastatic potential of tumor cells spatially and temporally. This work will result in both improved clinical stratification and, downstream from it, in a more robust set of targetable pathways for prevention of brain metastasis from breast and other primary sites.